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A little less than a year ago I wrote a blog post on my conversation with Mark Boguski, a pathologist at Harvard Medical School and the Chief Medical Officer at Genome Health Solutions, regarding Cancer and the fallacy of the $1000 genome. Mark recently sent me an email from a healthcare conference he described as “very self-consciously hip with a lot of contrived wonder and enthusiasm” and I knew we had the makings of another interesting conversation and related blog post. This time on the subject of ’s Watson project for healthcare .

Mark and I have had several conversations on what IBM's Watson is and isn’t in healthcare. The following is a summary of the conversation we had regarding efforts to use Watson for improving the state of human medicine. This is a subject that is especially relevant to me – as many of my clients often tell me “…but we’re waiting for Watson” when we sit down to talk about how we can help them advance their health IT.

Doctors using computers as “expert systems” to help them diagnose and treat disease has a history that goes back about 60 years. One of the pioneers of this field was the late Robert Ledley whose book on the Use of Computers in Biology and Medicine was published in 1965. Artificial intelligence as a branch of computer science enjoyed a surge of interest and attention in the mid-1980s and there was a corresponding surge of medical applications. The IBM Watson “cognitive computing” system has been heralded as the latest breakthrough in clinical “decision support.”

Last October, while attending and speaking at ’s Whitney Symposium at the company’s Global Research headquarters in Niskayuna, N.Y., Mark tells me he had the pleasure of hearing about the development of Watson in an after-dinner presentation by its principal investigator Dave Ferrucci. Dr. Ferrucci (pictured six from the left, standing next to Mark, in front of Thomas Edison’s desk) is a computer scientist and IBM fellow and gave a marvelous and animated talk on the trials and tribulations of developing his Jeopardy!-winning work. One couldn’t help but be impressed by the very clever and sophisticated combination of heuristics that led to his triumph.

But when his talk turned toward real-world, practical applications in healthcare, Mark began to get uneasy – he describes being struck by the idea that the IBM Watson team were a bit like generals preparing to fight the last war.

In the ensuing couple of months following the Whitney Symposium, we’ve all noticed a number of articles in publications like Wired, Fast Company the most recently The Atlantic describing how IBM’s Watson could outperform doctors because it can read all existing medical journals and textbooks in less time than the doctor’s coffee break. But there’s a serious and largely unrecognized problem emerging that will affect both human doctors and Watson alike: advanced diagnostic technologies are now capable of producing so much data that much of its meaning is not in the literature and it never will be. Traditional biomedical literature, as the place to discover the meaning of a new or unexpected finding, is rapidly approaching a Valley of Death. An example will explain why.

Articles about Watson’s medical potential regularly cite examples from oncology where Watson flags an unexpected, even counter intuitive “driver” mutation in the lung cancer of a (simulated) patient. This finding, supported by an association Watson discovers in the literature, suggests a new treatment option that most physicians wouldn’t even consider because that mutation is normally associated with not lung, but colon cancer. This example relies on the assumption that only a few “single gene” diagnostic tests will be performed on the tumor and, more importantly, that there is evidence already available in published sources associating these genes with treatments. But it’s already becoming commonplace to scan biopsies, regardless of the tumor type, for dozens to hundreds or even thousands of genes because the technology to do so is becoming more affordable and this approach represents the most parsimonious use of time, tissue and other resources. In this scenario, however, there can be many unexpected findings that have not been reported in medical journals and likely never will be the subject of published follow-up studies because the problem of researching these multitudinous findings simply cannot scale.

This gap, created by our ability to generate data much faster than we can ever ascribe meaning to it via traditional approaches, has been evident in biomedicine since the late 1990s (see Figure 1 here). Now, however, this gap has become a “Valley of Death,” in the application of new diagnostic technologies that produce more data than we can ever hope to interpret by consulting the literature, simply because there is no literature pertaining to it and perhaps never will be.

There is a way out of the Valley of Death, however, but it will require both human doctors and technology to approach the problem differently. Instead of relying only on the literature, we can and should develop and utilize computational methods to reverse engineer disease pathways from observational data, followed by simulated therapeutic interventions that will guide real treatments (see Figure 1 here). It is already possible to do this, in a limited and qualitative way, in situations where disease pathways are well-understood and existing drugs can be repurposed (eg. Off label usage…) in real time.

There are several groups of researchers and companies focused on addressing pieces of the puzzle. What makes this approach relevant is its ability to wrap multiple knowledge bases in a very clever piece of heuristics-based programming. If we think of Watson as a framework and integration engine for aggregating medical big data sources we may be able to fight the next war instead of the last one. Thinking of a desired future state for Watson that incorporates more big data sources, as opposed to just published literature, could advance diagnostic medicine by leaps and bounds. Or, as Mark might say, “now we’re bringing a gun to a knife fight.”